Flow charts illustrate the steps of a process and how the steps are related to each other. It can be used to describe the process, increase a team’s knowledge of the entire process, identify weaknesses or breakdown points in the current process, or design a new process. An example of a flow chart outlining how adverse drug reactions might be addressed within an organization is provided below.
Pareto charts are vertical bar graphs with the data presented so that the bars are arranged from left to right on the horizontal axis in their order of decreasing frequency. This arrangement helps identify which problems to address in what order. By addressing the data represented in the tallest bars (e.g., the most frequently occurring problems or contributing factors) efforts can be focused on areas where the most gain can be realized. Pareto charts are commonly used to identify issues to address, delineate potential causes of a problem, and monitor improvements in processes. An example of a Pareto chart is provided below. This example illustrates frequently occurring factors contributing to improper dose medication errors. By focusing on transcription errors as a contributing factor on which to focus quality improvement efforts, the quality improvement team will generally gain more than by tackling the smaller bars.
FISHBONE OR CAUSE-AND-EFFECT DIAGRAM
Fishbone or cause-and-effect diagrams represent the relationship between an outcome (represented at the head of the fish) and the possible causes of the outcome (represented as the bones of the fish). The bones of the fish should represent causes and not symptoms of the issue. Fishbone diagrams are commonly used to identify components of a process to address, delineate potential causes of a problem, or identify practitioner groups that participate in producing an outcome and should be represented in the group addressing quality issues in the process(es). An example of a Fishbone chart is provided below.
Control charts are run charts or line graphs with defined allowable limits of variation. Data are plotted on the graph as they become available with new data points connected to older data by a continuous line. The x-axis is usually a measure of time. The control limits help identify which variations in data are important. Control limits are statistically determined based on average ranges and sample size. Fluctuation in data points above and below the average is expected and is referred to as common variation or common cause as long as they remain between the control limits. Data points above the upper control limit or below the lower control limit are referred to as special variation or special cause. Special cause variation indicates that something different is going on outside the normal operation of the process. Also, a series of data points above or below average may indicate a trend in performance that may need to be addressed. As variability in a process is reduced by quality improvement efforts, control limits should be recalculated (and narrowed) based on ongoing data. An example of a control chart is provided below. Calls from pharmacists to prescribers in response to questions or issues related to new medication orders are represented over a 6-month period. Data from the month of July indicates a significant increase in the number of calls made. A quality improvement team evaluating this data would then attempt to identify what contributed to this increase. A potential cause in many institutions might be the influx of new medical housestaff into the organization each July. One potential intervention to reduce this special cause is to improve the orientation of new practitioners to the medication use process within the organization.